Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS | Paper Citation Record

Observation 390ee41d-4794-4a04-a32c-9563cdb3b300 · outbound

This paper cites A comprehen- sive systematic literature review on intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS A comprehen- sive systematic literature review on intrusion detection systems,

Reference 1

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dc638f54-1c51-4c1b-9382-58509deb7b4d · outbound

This paper cites A survey on data-driven network intrusion detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS A survey on data-driven network intrusion detection,

Reference 2

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73664678-d250-4220-922e-814618ca3879 · outbound

This paper cites Generative adversarial attacks against intrusion detection systems using active learning,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Generative adversarial attacks against intrusion detection systems using active learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:12.375589Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ee06155-e6f2-4c70-bdc0-235011fd1f83 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Explaining and Harnessing Adversarial Examples

Reference 4

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This paper cites Adversarial examples: Opportunities and chal- lenges,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial examples: Opportunities and chal- lenges,

Reference 5

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Observation 25b7413c-9da9-4ae9-bb02-7d9d9f07646e · outbound

This paper cites Bringing a gan to a knife-fight: Adapting malware communication to avoid detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Bringing a gan to a knife-fight: Adapting malware communication to avoid detection,

Reference 6

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b345d6ca-41ac-4696-a187-33121b21909b · outbound

This paper cites Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects

Reference 7

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Observation 26e47f4a-0acf-4fac-a9d1-932532a64c33 · outbound

This paper cites Modeling realistic adversarial attacks against network intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Modeling realistic adversarial attacks against network intrusion detection systems,

Reference 8

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Observation bf661ebe-6d7f-4cf1-9a95-5bd955e5a747 · outbound

This paper cites A gradient- based approach for adversarial attack on deep learning-based network intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS A gradient- based approach for adversarial attack on deep learning-based network intrusion detection systems,

Reference 9

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Observation c50048d9-f00d-4855-bece-283887ca85f4 · outbound

This paper cites Adversarial machine learning in network intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial machine learning in network intrusion detection systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:11.201334Z

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Observation d0929150-1a9e-4a6d-b499-84b66211881f · outbound

This paper cites Adversarial examples for network intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial examples for network intrusion detection systems,

Reference 11

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Observation a4127ede-945f-4d04-aa36-b6c524235dbf · outbound

This paper cites Adaptative perturbation patterns: Realistic adversarial learning for robust intrusion detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adaptative perturbation patterns: Realistic adversarial learning for robust intrusion detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:10.622237Z

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Observation e1c30c7a-0c80-4cc8-92c5-7b665a8f8e3a · outbound

This paper cites Adversarial machine learning- industry perspectives,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial machine learning- industry perspectives,

Reference 13

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Observation 464dddf1-3e9c-4b6b-87dd-24ae8ad6fbfc · outbound

This paper cites Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:10.327120Z

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Observation 7aeb2688-83ba-484e-802c-95e3a479979d · outbound

This paper cites Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T22:48:57.253228Z

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Observation 7914913c-732f-4f5f-a381-2245bef12244 · outbound

This paper cites Adversarial machine learning applied to intrusion and malware scenarios: a systematic review,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial machine learning applied to intrusion and malware scenarios: a systematic review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:10.058616Z

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Observation 980c88c6-0fe1-4340-a98a-94556b94f6b1 · outbound

This paper cites A survey on the vulnerability of deep neural networks against adversarial attacks,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS A survey on the vulnerability of deep neural networks against adversarial attacks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:09.667349Z

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Observation b513cb4b-03cc-40ac-b86d-e8a9361a3311 · outbound

This paper cites The limitations of deep learning in adversarial settings,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS The limitations of deep learning in adversarial settings,

Reference 18

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Observation 2fd2c7a9-e601-4a42-b503-2b9cf0074e6f · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

Reference 19

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Observation 3e5df237-0b24-41c4-b326-6dc8cc5cb3ec · outbound

This paper cites Hopskipjumpattack: A query-efficient decision-based attack,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Hopskipjumpattack: A query-efficient decision-based attack,

Reference 20

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Observation 7f9c8402-2405-4df8-9c27-da7cedc4be98 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

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Observation 98f4ded0-1ba4-4f35-8cd0-8a5e44d035da · outbound

This paper cites Improving adversarial ro- bustness via promoting ensemble diversity,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Improving adversarial ro- bustness via promoting ensemble diversity,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:09.351678Z

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Observation 253e5838-3d61-4dfb-bf56-e3bebc283846 · outbound

This paper cites On Detecting Adversarial Perturbations.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS On Detecting Adversarial Perturbations

Reference 23

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Observation 8be3120a-f5c8-4b18-a7dc-6bd28035557f · outbound

This paper cites Evaluating adversarial robustness of secret key-based defenses,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Evaluating adversarial robustness of secret key-based defenses,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:09.088942Z

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Observation 47c28566-3abe-45f8-b4b2-f02fcea8b19c · outbound

This paper cites Adversarial attacks and defenses in image classification: A practical perspective,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial attacks and defenses in image classification: A practical perspective,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:08.876889Z

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Observation 768bedd8-e18d-4c50-85cc-4241ebfd2398 · outbound

This paper cites Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Reference 26

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Observation 7e5b130f-4a71-40b1-962d-97800a57803b · outbound

This paper cites Query efficient black- box adversarial attack on deep neural networks,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Query efficient black- box adversarial attack on deep neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:08.659007Z

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Observation e35e39db-3b76-4484-82ba-2386f984fc4d · outbound

This paper cites The logbarrier adversarial attack: making effective use of decision boundary information,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS The logbarrier adversarial attack: making effective use of decision boundary information,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:08.476644Z

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Observation 087f8c3b-9e9d-4e28-a341-e033f9d316b0 · outbound

This paper cites Improving the robust- ness of adversarial attacks using an affine-invariant gradient estimator,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Improving the robust- ness of adversarial attacks using an affine-invariant gradient estimator,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:08.201971Z

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Observation 7329dedc-ccb6-4e82-94fd-f813e593de30 · outbound

This paper cites Towards transferable targeted adversarial examples,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Towards transferable targeted adversarial examples,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:07.916140Z

Source-reported events for the cited work

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Observation c343bec9-698b-4f84-bfc9-ee8d7536d3a8 · outbound

This paper cites A complete list of all (arxiv) adversarial example papers,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS A complete list of all (arxiv) adversarial example papers,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:07.722624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9fa8cf17-ebc9-4a80-8bf3-d0fc58ee6ddd · outbound

This paper cites Adver- sarial attacks on machine learning cybersecurity defences in industrial control systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adver- sarial attacks on machine learning cybersecurity defences in industrial control systems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:07.480180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e589e07f-ec28-4783-93d5-edb9746400c8 · outbound

This paper cites Untargeted white-box adver- sarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Untargeted white-box adver- sarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:07.233682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ca32139-f2d3-49c6-a5b3-7a8d8afcbd1e · outbound

This paper cites Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untar- geted white-box optimization adversarial attack,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untar- geted white-box optimization adversarial attack,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:06.998363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e80cd6d9-6644-439b-99d2-390ad55d62ff · outbound

This paper cites Defending network intrusion detection systems against adversarial evasion attacks,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Defending network intrusion detection systems against adversarial evasion attacks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:06.797098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 650f2863-cfc1-42b8-a830-a6733ba3b350 · outbound

This paper cites Argan-ids: Adversarial resistant intrusion detection systems using generative adversarial networks,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Argan-ids: Adversarial resistant intrusion detection systems using generative adversarial networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:06.583958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5887aae7-19b9-40c7-95de-67747c2ecdca · outbound

This paper cites Adversarial attacks against deep learning-based network intrusion detection systems and defense mechanisms,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial attacks against deep learning-based network intrusion detection systems and defense mechanisms,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:06.317294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a26f087-3cd1-43a9-8f67-3601c269bd13 · outbound

This paper cites Adversarial attack against dos intrusion detection: An improved boundary-based method,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adversarial attack against dos intrusion detection: An improved boundary-based method,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:06.093876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c94e679a-3a73-488a-9611-ef01509f5462 · outbound

This paper cites Evading machine learning botnet detection models via deep reinforcement learning,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Evading machine learning botnet detection models via deep reinforcement learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.931225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7869a959-4cb8-4063-be37-e871c599fc34 · outbound

This paper cites Tantra: Timing-based adversarial network traffic reshaping attack,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Tantra: Timing-based adversarial network traffic reshaping attack,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.746495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 089eb51c-985a-4cfd-92c4-5e35cbf05d7f · outbound

This paper cites Adv-bot: Realistic adversarial botnet attacks against network intrusion detection systems,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Adv-bot: Realistic adversarial botnet attacks against network intrusion detection systems,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.600125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1cd80266-442e-4d73-a302-7110742b4144 · outbound

This paper cites Idsgan: Generative adversarial networks for attack generation against intrusion detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Idsgan: Generative adversarial networks for attack generation against intrusion detection,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.427874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a69deb41-975c-46e4-b2ee-ad2a11ee8d4e · outbound

This paper cites Black-box adversarial transferability: An empirical study in cybersecurity perspective,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Black-box adversarial transferability: An empirical study in cybersecurity perspective,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.282618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation caff6795-7265-4c02-ba1e-dd7bff2f2b4d · outbound

This paper cites Toward transferable adversarial attacks against autoencoder-based network intrusion detectors,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Toward transferable adversarial attacks against autoencoder-based network intrusion detectors,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:05.113716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39d0d207-7c33-42ca-a9af-f7339209a596 · outbound

This paper cites Nids-vis: Improving the gen- eralized adversarial robustness of network intrusion detection system,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Nids-vis: Improving the gen- eralized adversarial robustness of network intrusion detection system,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:04.951171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74281f78-f9dc-4f6c-9015-79209cee4a64 · outbound

This paper cites Generating Practical Adversarial Network Traffic Flows Using NIDSGAN.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Generating Practical Adversarial Network Traffic Flows Using NIDSGAN

Reference 46

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Source-reported events for the cited work

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Observation 3f98e37e-c3a8-4549-8ed7-c02e223d446b · outbound

This paper cites Multiple change-point detection: a selective overview,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Multiple change-point detection: a selective overview,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:04.725291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f75bbc0-3109-4c66-91e8-f401963a3e90 · outbound

This paper cites Seeded binary segmenta- tion: a general methodology for fast and optimal changepoint detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Seeded binary segmenta- tion: a general methodology for fast and optimal changepoint detection,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:04.474377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ada7ea49-f0f2-4b77-8619-e2c21c3ae21f · outbound

This paper cites Causal inference for time series,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Causal inference for time series,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:01.723610Z

Source-reported events for the cited work

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Observation e9d3af97-02aa-4f3a-98fb-56c42a838db9 · outbound

This paper cites Ordinary least-squares (ols) model,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Ordinary least-squares (ols) model,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:04.242382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eb669993-d7d8-4b3d-b117-d1089175c6c7 · outbound

This paper cites Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:03.921042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18e818e6-c707-4202-b73a-eb1718a5657c · outbound

This paper cites Deep isolation forest for anomaly detection,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Deep isolation forest for anomaly detection,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:02.073356Z

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Unavailable: canonical work link unavailable.

Observation 6d7a1022-1698-4b9e-a0aa-4d7820d7508b · outbound

This paper cites Sign-OPT: A Query-Efficient Hard-label Adversarial Attack.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Sign-OPT: A Query-Efficient Hard-label Adversarial Attack

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:02.191727Z

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Unavailable: canonical work link unavailable.

Observation fe622020-75d5-44e2-99d0-7ab97411836d · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Towards evaluating the robustness of neural networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:03.673568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba99617f-b446-48cb-b0d6-6a9cb955fac5 · outbound

This paper cites Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:02.511616Z

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Unavailable: canonical work link unavailable.

Observation ec2581d9-2451-4fc6-93e0-fb735c1cb218 · outbound

This paper cites Genattack: Practical black-box attacks with gradient- free optimization,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Genattack: Practical black-box attacks with gradient- free optimization,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:03.416692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b8514a4a-2f8a-4ba0-935e-c70e7210b41a · outbound

This paper cites Evolu- tionary algorithms,.

Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS Evolu- tionary algorithms,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:03.173183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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